William greene stern school of business new york university l.jpg
Sponsored Links
This presentation is the property of its rightful owner.
1 / 34

Efficiency Measurement PowerPoint PPT Presentation


  • 77 Views
  • Updated On :
  • Presentation posted in: General

William Greene Stern School of Business New York University. Efficiency Measurement. Lab Session 2. Stochastic Frontier Estimation. Application to Spanish Dairy Farms. N = 247 farms, T = 6 years (1993-1998). Using Farm Means of the Data. OLS vs. Frontier/MLE. JLMS Inefficiency Estimator.

Download Presentation

Efficiency Measurement

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


William Greene

Stern School of Business

New York University

Efficiency Measurement


Lab Session 2

Stochastic Frontier Estimation


Application to Spanish Dairy Farms

N = 247 farms, T = 6 years (1993-1998)


Using Farm Means of the Data


OLS vs. Frontier/MLE


JLMS Inefficiency Estimator

FRONTIER ; LHS = the variable

; RHS = ONE, the variables

; EFF = the new variable $

Creates a new variable in the data set.

FRONTIER ; LHS = YIT ; RHS = X ; EFF = U_i $

Use ;Techeff = variable to compute exp(-u).


Confidence Intervals for Technical Inefficiency, u(i)


Prediction Intervals for Technical Efficiency, Exp[-u(i)]


Prediction Intervals for Technical Efficiency, Exp[-u(i)]


Compare SF and DEA


Similar, but differentwith a crucial pattern


The Dreaded Error 315 – Wrong Skewness


Cost Frontier Model


Linear Homogeneity Restriction


Translog vs. Cobb Douglas


Cost Frontier Command

FRONTIER ; COST

; LHS = the variable

; RHS = ONE, the variables

; EFF = the new variable $

ε(i) = v(i) + u(i) [u(i) is still positive]


Estimated Cost Frontier: C&G


Cost Frontier Inefficiencies


Normal-Truncated NormalFrontier Command

FRONTIER [; COST]

; LHS = the variable

; RHS = ONE, the variables

; Model = Truncation

; EFF = the new variable $

ε(i) = v(i) +/- u(i)

u(i) = |U(i)|, U(i) ~ N[μ,2]

The half normal model has μ = 0.


Observations

  • Truncation Model estimation is often unstable

    • Often estimation is not possible

    • When possible, estimates are often wild

  • Estimates of u(i) are usually only moderately affected

  • Estimates of u(i) are fairly stable across models (exponential, truncation, etc.)


Truncated Normal Model ; Model = T


Truncated Normal vs. Half Normal


Multiple Output Cost Function


Ranking Observations

CREATE ; newname = Rnk ( Variable ) $

Creates the set of ranks. Use in any subsequent analysis.


  • Login